
How a multiplex network can be efficiently explored to create a node context? We provide multiple random walk strategies that effectively navigate through the layered structure of the networks. The crux of such strategies lies in devising a random walker's transition probability to account for in-layer traversal and a switching probability for cross-layered movement.

How large scale multiplex networks can be represented in a lower dimensional space? We propose Multi-Net a fast, scalable, deep network embedding algorithm that preserves the cross-layered neighborhood of a node across its various interaction patterns.

How to reconstruct a layer in a multiplex network using Multi-Net framework? By extracting distributed feature representations of a node in a multilayered neighborhood preserving context and its structural arrangement in \({L}-1\)  layers, we use Multi-Net to effectively infer the complete structure of the \({L}^{th}\)  network layer. Our experiments show that our models outperform recently proposed multiplex network embedding methods OhmNet [1]} and MNE [2]} by approximately 11% accuracy. To our knowledge, this is the first work to evaluate multiple random walks for multiplex network embedding and compare against existing models.

